mlsanityData to 7 Oct 2026

Theory of Space: Can Foundation Models Construct Spatial Beliefs through Active Exploration?

arXiv 2602.07055 ↗First posted 4 Feb 2026cs.AI cs.CL cs.LGcited by 18

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Abstract (arXiv)

Spatial embodied intelligence requires agents to act to acquire information under partial observability. While multimodal foundation models excel at passive perception, their capacity for active, self-directed exploration remains understudied. We propose Theory of Space, defined as an agent's ability to actively acquire information through self-directed, active exploration and to construct, revise, and exploit a spatial belief from sequential, partial observations. We evaluate this through a benchmark where the goal is curiosity-driven exploration to build an accurate cognitive map. A key innovation is spatial belief probing, which prompts models to reveal their internal spatial representations at each step. Our evaluation of state-of-the-art models reveals several critical bottlenecks. First, we identify an Active-Passive Gap, where performance drops significantly when agents must autonomously gather information. Second, we find high inefficiency, as models explore unsystematically compared to program-based proxies. Through belief probing, we diagnose that while perception is an initial bottleneck, global beliefs suffer from instability that causes spatial knowledge to degrade over time. Finally, using a false belief paradigm, we uncover Belief Inertia, where agents fail to update obsolete priors with new evidence. This issue is present in text-based agents but is particularly severe in vision-based models. Our findings suggest that current foundation models struggle to maintain coherent, revisable spatial beliefs during active exploration.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 39 of its 66 references that are in the map. First posted on arXiv 4 Feb 2026.

Builds on

39 of 66 references.

References · 39

  1. 2025InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiencycited by 1,419
  2. 2020ALFWorld: Aligning Text and Embodied Environments for Interactive Learningcited by 1,143
  3. 2017Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environmentscited by 1,013
  4. 2019ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Taskscited by 773
  5. 2024SpatialVLM: Endowing Vision-Language Models with Spatial Reasoning Capabilitiescited by 679
  6. 2024Thinking in Space: How Multimodal Large Language Models See, Remember, and Recall Spacescited by 580
  7. 2021CALVIN: A Benchmark for Language-Conditioned Policy Learning for Long-Horizon Robot Manipulation Taskscited by 564
  8. 2020Beyond the Nav-Graph: Vision-and-Language Navigation in Continuous Environmentscited by 429
  9. 2018VirtualHome: Simulating Household Activities via Programscited by 411
  10. 2022VIMA: General Robot Manipulation with Multimodal Promptscited by 403
  11. 2020Room-Across-Room: Multilingual Vision-and-Language Navigation with Dense Spatiotemporal Groundingcited by 401
  12. 2017Embodied Question Answeringcited by 335
27 more
  1. 2019REVERIE: Remote Embodied Visual Referring Expression in Real Indoor Environmentscited by 330
  2. 2015Towards AI-Complete Question Answering: A Set of Prerequisite Toy Taskscited by 309
  3. 2024SpatialRGPT: Grounded Spatial Reasoning in Vision Language Modelscited by 297
  4. 2020ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulationcited by 249
  5. 2023What's "up" with vision-language models? Investigating their struggle with spatial reasoningcited by 245
  6. 2025EmbodiedBench: Comprehensive Benchmarking Multi-modal Large Language Models for Vision-Driven Embodied Agentscited by 213
  7. 2021TEACh: Task-driven Embodied Agents that Chatcited by 194
  8. 2025MMSI-Bench: A Benchmark for Multi-Image Spatial Intelligencecited by 169
  9. 2021BEHAVIOR: Benchmark for Everyday Household Activities in Virtual, Interactive, and Ecological Environmentscited by 161
  10. 2017IQA: Visual Question Answering in Interactive Environmentscited by 154
  11. 20243DSRBench: A Comprehensive 3D Spatial Reasoning Benchmarkcited by 133
  12. 2019Stay on the Path: Instruction Fidelity in Vision-and-Language Navigationcited by 129
  13. 2025RoboRefer: Towards Spatial Referring with Reasoning in Vision-Language Models for Roboticscited by 125
  14. 2023SmartPlay: A Benchmark for LLMs as Intelligent Agentscited by 123
  15. 2021SpartQA: : A Textual Question Answering Benchmark for Spatial Reasoningcited by 75
  16. 2024Explore until Confident: Efficient Exploration for Embodied Question Answeringcited by 74
  17. 2022StepGame: A New Benchmark for Robust Multi-Hop Spatial Reasoning in Textscited by 65
  18. 2022DialFRED: Dialogue-Enabled Agents for Embodied Instruction Followingcited by 63
  19. 2025Spatial Reasoning with Vision-Language Models in Ego-Centric Multi-View Scenescited by 62
  20. 2024Reasoning Paths with Reference Objects Elicit Quantitative Spatial Reasoning in Large Vision-Language Modelscited by 54
  21. 2025Multi-SpatialMLLM: Multi-Frame Spatial Understanding with Multi-Modal Large Language Modelscited by 52
  22. 2025VLM4D: Towards Spatiotemporal Awareness in Vision Language Modelscited by 43
  23. 2025CityEQA: A Hierarchical LLM Agent on Embodied Question Answering Benchmark in City Spacecited by 31
  24. 2025InternSpatial: A Comprehensive Dataset for Spatial Reasoning in Vision-Language Modelscited by 26
  25. 2024Reframing Spatial Reasoning Evaluation in Language Models: A Real-World Simulation Benchmark for Qualitative Reasoningcited by 16
  26. 2024ReALFRED: An Embodied Instruction Following Benchmark in Photo-Realistic Environmentscited by 16
  27. 2025Enter the Mind Palace: Reasoning and Planning for Long-term Active Embodied Question Answeringcited by 12

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 18.

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  1. 2026Done, But Not Sure: Disentangling World Completion from Self-Termination in Embodied Agentscited by 7
  2. 2026SpaMEM: Benchmarking Dynamic Spatial Reasoning via Perception-Memory Integration in Embodied Environmentscited by 6
  3. 2026The Limits of Learning from Pictures and Text: Vision-Language Models and Embodied Scene Understandingcited by 2
  4. 2026Exploration and Exploitation Errors Are Measurable for Language Model Agentscited by 2
  5. 20263D Primitives are a Spatial Language for VLMscited by 2
  6. 2026Planning with the Viewscited by 2
  7. 2026When Should Models Change Their Minds? Contextual Belief Management in Large Language Modelscited by 2
  8. 2026SpaceMind++: Toward Allocentric Cognitive Maps for Spatially Grounded Video MLLMscited by 1
  9. 2026SpatialAct: Probing Spatial Reasoning-to-Action Capabilities of VLM Agents in 3D Scenescited by 1
  10. 2026ScoutVLA: UAV-Centric Active Perception via a Dual-Expert VLA Model for Open-World Embodied Question Answeringcited by 1
  11. 2026From Hallucination to Grounding: Diagnosing Visual Spatial Intelligence via CRISPcited by 1
  12. 2026Theory of Code Space: Do Code Agents Understand Software Architecture?cited by 0
6 more
  1. 2026Where to Look: Can Foundation Models Reach a Target Viewpoint Through Active Exploration?cited by 0
  2. 2026OmniSpace: Efficient Geometry Awareness for Autonomous Vehicles MLLMscited by 0
  3. 2026Explore, Map, Remember, Decide: Are Embodied VLMs Ready for Safety-Critical Scenarios?cited by 0
  4. 2026VABench: Measuring Embodied Spatial Intelligence through Visual Demonstrations, Active Perception, and Metric Controlcited by 0
  5. 2026DriveHierarchy: A Benchmark for Diagnosing VLM Driving Capabilities from Open-Loop Understanding to Closed-Loop Executioncited by 0
  6. 2026Who Is Left of Whom? Tracing Spatial Evidence and Role Binding in Relative-Position Reasoningcited by 0